This paper reviews the current state of the art in artificial intelligence (AI) technologies and
applications in the context of the creative industries. A brief background of AI, and …

Deep learning techniques have received much attention in the area of image denoising.
However, there are substantial differences in the various types of deep learning methods …

Blind face restoration is a highly ill-posed problem that often requires auxiliary guidance to
1) improve the map** from degraded inputs to desired outputs, or 2) complement high …

Face super-resolution (FSR) aims to reconstruct high-resolution (HR) face images from the
low-resolution (LR) ones. With the advent of deep learning, the FSR technique has achieved …

Blind face restoration usually synthesizes degraded low-quality data with a pre-defined
degradation model for training, while more complex cases could happen in the real world …

Blind face restoration usually relies on facial priors, such as facial geometry prior or
reference prior, to restore realistic and faithful details. However, very low-quality inputs …

Blind face restoration (BFR) from severely degraded face images in the wild is a very
challenging problem. Due to the high illness of the problem and the complex unknown …

Although generative facial prior and geometric prior have recently demonstrated high-quality
results for blind face restoration, producing fine-grained facial details faithful to inputs …

Arbitrary attribute editing generally can be tackled by incorporating encoder-decoder and
generative adversarial networks. However, the bottleneck layer in encoder-decoder usually …

While deep learning-based methods for blind face restoration have achieved
unprecedented success, they still suffer from two major limitations. First, most of them …